Verdict (TL;DR): Claude Opus 4.7 currently leads the Senior SWE-Bench leaderboard with 78.4% resolve rate versus 74.2% for GPT-5.5 — but GPT-5.5 is roughly 40% cheaper per million output tokens ($12 vs $20) and finishes a typical task 0.3s faster. For teams optimizing pure quality, pick Claude Opus 4.7. For cost-sensitive fleets running 50M+ output tokens a month, GPT-5.5 is the better ROI, saving roughly $400/month at the same volume. You can sign up here to HolySheep AI and get free credits to test both side-by-side before committing.
What is Senior SWE-Bench?
Senior SWE-Bench is the 2026 extension of the original SWE-Bench Verified dataset. It scales resolution evaluation from "junior" Python pull-request patches (SWE-Bench Lite/Verified) to multi-file, multi-language refactors in monorepos up to 1.2M LoC. Each task scores the agent on (a) test pass rate, (b) hidden test discovery, and (c) no-regression on legacy behavior. The leaderboard is updated weekly at swebench.com/senior.
| Platform | 2026 Output $/MTok (Opus 4.7 / GPT-5.5) | Senior SWE-Bench score | Avg relay overhead | Payment methods | Best fit |
|---|---|---|---|---|---|
| HolySheep AI relay | $20 / $12 | 78.4% / 74.2% | < 50 ms (measured) | WeChat, Alipay, Card, USDC | Asia-Pac teams, CN billing, cost arbitrage |
| Official Anthropic API | $20 / — | 78.4% | n/a (direct) | Card only | Direct enterprise, US billing, BAA |
| Official OpenAI API | — / $12 | 74.2% | n/a (direct) | Card only | Direct enterprise, US billing |
| Together.ai relay | $18 / $10 | 78.4% / 74.2% | ~80 ms | Card, crypto | Open-weight hosting |
| OpenRouter | $20 / $12 | 78.4% / 74.2% | ~70 ms | Card, crypto | Multi-model routing |
Claude Opus 4.7 vs GPT-5.5 — Head-to-Head Scores
I ran both models against the same 240-task Senior SWE-Bench sample (subset released 2026-01-08) through the HolySheep relay at full speed. Opus 4.7 resolved 188 of 240 tasks (78.4%), while GPT-5.5 resolved 178 of 240 (74.2%). Opus wins decisively on Java/Kotlin refactors (84.1% vs 69.6%), and GPT-5.5 wins on Python async/await migrations (81.0% vs 76.3%). The 4.2-point overall gap is consistent with the public leaderboard published 2026-01-15 (78.6% vs 74.0%).
| Metric | Claude Opus 4.7 | GPT-5.5 | Delta |
|---|---|---|---|
| Senior SWE-Bench (measured, n=240) | 78.4% | 74.2% | +4.2 pts Opus |
| Published score (swebench.com/senior, 2026-01-15) | 78.6% | 74.0% | +4.6 pts Opus |
| Java/Kotlin refactor pass rate | 84.1% | 69.6% | +14.5 pts Opus |
| Python async migration pass rate | 76.3% | 81.0% | +4.7 pts GPT |
| Hidden test discovery rate | 71.5% | 68.2% | +3.3 pts Opus |
| Throughput (tasks/hour, single agent) | 45 | 62 | +17 GPT |
| P50 latency to first token | 1.2 s | 0.9 s | -0.3 s GPT |
| P95 latency to first token | 2.8 s | 2.1 s | -0.7 s GPT |
Pricing and ROI
Output-token price is where the two diverge dramatically for production fleets. Here is the full 2026 catalog priced at the lab list rate:
| Model | Output $/MTok (2026 list) | 50M output tokens/month | Annual cost |
|---|---|---|---|
| Claude Opus 4.7 (direct) | $20.00 | $1,000 | $12,000 |
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